Customized Mid-Air Gestures for Accessibility: A $B Recognizer for Multi-Dimensional Biosignal Gestures

TL;DR

This paper introduces the $B recognizer, a biosignal-based mid-air gesture recognition system that simplifies the process for users with disabilities, achieving high accuracy without requiring domain expertise.

Contribution

The $B recognizer enables accessible biosignal gesture recognition without domain expertise, outperforming traditional methods and matching deep learning in user-independent scenarios.

Findings

Achieved > 95% recognition rate in user-dependent conditions.

Outperformed traditional machine learning algorithms.

Performed comparably to deep learning in user-independent conditions.

Abstract

Biosignal interfaces, using sensors in, on, or around the body, promise to enhance wearables interaction and improve device accessibility for people with motor disabilities. However, biosignals are multi-modal, multi-dimensional, and noisy, requiring domain expertise to design input features for gesture classifiers. The \$B-recognizer enables mid-air gesture recognition without needing expertise in biosignals or algorithms. \$B resamples, normalizes, and performs dimensionality reduction to reduce noise and enhance signals relevant to the recognition. We tested \$B on a dataset of 26 participants with and 8 participants without upper-body motor disabilities performing personalized ability-based gestures. For two conditions (user-dependent, gesture articulation variability), \$B outperformed our comparison algorithms (traditional machine learning with expert features and deep learning), with > 95% recognition rate. For the user-independent condition, \$B and deep learning performed comparably for participants with disabilities. Our biosignal dataset is publicly available online. $B highlights the potential and feasibility of accessible biosignal interfaces.